class_docstr: Word embedding model.
docstr: Converts a given word2vec model into H2OFrame.
  
  :returns: a frame representing learned word embeddings.
  
  :examples:
  
  >>> words = h2o.create_frame(rows=1000,cols=1,string_fraction=1.0,missing_fraction=0.0)
  >>> embeddings = h2o.create_frame(rows=1000,cols=100,real_fraction=1.0,missing_fraction=0.0)
  >>> word_embeddings = words.cbind(embeddings)
  >>> w2v_model = H2OWord2vecEstimator(pre_trained=word_embeddings)
  >>> w2v_model.train(training_frame=word_embeddings)
  >>> w2v_frame = w2v_model.to_frame()
  >>> word_embeddings.names = w2v_frame.names
  >>> word_embeddings.as_data_frame().equals(word_embeddings.as_data_frame())
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code: def to_frame(self):
      """
      Converts a given word2vec model into H2OFrame.
  
      :returns: a frame representing learned word embeddings.
  
      :examples:
  
      >>> words = h2o.create_frame(rows=1000,cols=1,string_fraction=1.0,missing_fraction=0.0)
      >>> embeddings = h2o.create_frame(rows=1000,cols=100,real_fraction=1.0,missing_fraction=0.0)
      >>> word_embeddings = words.cbind(embeddings)
      >>> w2v_model = H2OWord2vecEstimator(pre_trained=word_embeddings)
      >>> w2v_model.train(training_frame=word_embeddings)
      >>> w2v_frame = w2v_model.to_frame()
      >>> word_embeddings.names = w2v_frame.names
      >>> word_embeddings.as_data_frame().equals(word_embeddings.as_data_frame())
      """
      return h2o.H2OFrame._expr(
          expr=ExprNode("word2vec.to.frame", self)
      )
  
file_path: h2o-3/h2o-py/h2o/model/models/word_embedding.py
imports: from h2o.utils.compatibility import *
  from collections import OrderedDict
  import h2o
  from h2o.expr import ExprNode
  from h2o.model import ModelBase
label_desc: Use image node_id 3 for calling the H2OWordEmbeddingModel obj's underlying member method code with example usage: obj.to_frame() and returns: h2o